Final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#INCLUDE LIBRARIES\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "import itertools\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "from nltk import sent_tokenize, word_tokenize\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hello', 'everyone', '.', 'You', 'are', 'reading', 'NLP', 'article', '.']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.tokenize import word_tokenize\n",
    "text = \"Hello everyone. You are reading NLP article.\"\n",
    "word_tokenize(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8476</td>\n",
       "      <td>You Can Smell Hillary’s Fear</td>\n",
       "      <td>Daniel Greenfield, a Shillman Journalism Fello...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10294</td>\n",
       "      <td>Watch The Exact Moment Paul Ryan Committed Pol...</td>\n",
       "      <td>Google Pinterest Digg Linkedin Reddit Stumbleu...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3608</td>\n",
       "      <td>Kerry to go to Paris in gesture of sympathy</td>\n",
       "      <td>U.S. Secretary of State John F. Kerry said Mon...</td>\n",
       "      <td>REAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10142</td>\n",
       "      <td>Bernie supporters on Twitter erupt in anger ag...</td>\n",
       "      <td>— Kaydee King (@KaydeeKing) November 9, 2016 T...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>875</td>\n",
       "      <td>The Battle of New York: Why This Primary Matters</td>\n",
       "      <td>It's primary day in New York and front-runners...</td>\n",
       "      <td>REAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6903</td>\n",
       "      <td>Tehran, USA</td>\n",
       "      <td>\\nI’m not an immigrant, but my grandparents ...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7341</td>\n",
       "      <td>Girl Horrified At What She Watches Boyfriend D...</td>\n",
       "      <td>Share This Baylee Luciani (left), Screenshot o...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>95</td>\n",
       "      <td>‘Britain’s Schindler’ Dies at 106</td>\n",
       "      <td>A Czech stockbroker who saved more than 650 Je...</td>\n",
       "      <td>REAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4869</td>\n",
       "      <td>Fact check: Trump and Clinton at the 'commande...</td>\n",
       "      <td>Hillary Clinton and Donald Trump made some ina...</td>\n",
       "      <td>REAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2909</td>\n",
       "      <td>Iran reportedly makes new push for uranium con...</td>\n",
       "      <td>Iranian negotiators reportedly have made a las...</td>\n",
       "      <td>REAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                              title  \\\n",
       "0        8476                       You Can Smell Hillary’s Fear   \n",
       "1       10294  Watch The Exact Moment Paul Ryan Committed Pol...   \n",
       "2        3608        Kerry to go to Paris in gesture of sympathy   \n",
       "3       10142  Bernie supporters on Twitter erupt in anger ag...   \n",
       "4         875   The Battle of New York: Why This Primary Matters   \n",
       "5        6903                                        Tehran, USA   \n",
       "6        7341  Girl Horrified At What She Watches Boyfriend D...   \n",
       "7          95                  ‘Britain’s Schindler’ Dies at 106   \n",
       "8        4869  Fact check: Trump and Clinton at the 'commande...   \n",
       "9        2909  Iran reportedly makes new push for uranium con...   \n",
       "\n",
       "                                                text label  \n",
       "0  Daniel Greenfield, a Shillman Journalism Fello...  FAKE  \n",
       "1  Google Pinterest Digg Linkedin Reddit Stumbleu...  FAKE  \n",
       "2  U.S. Secretary of State John F. Kerry said Mon...  REAL  \n",
       "3  — Kaydee King (@KaydeeKing) November 9, 2016 T...  FAKE  \n",
       "4  It's primary day in New York and front-runners...  REAL  \n",
       "5    \\nI’m not an immigrant, but my grandparents ...  FAKE  \n",
       "6  Share This Baylee Luciani (left), Screenshot o...  FAKE  \n",
       "7  A Czech stockbroker who saved more than 650 Je...  REAL  \n",
       "8  Hillary Clinton and Donald Trump made some ina...  REAL  \n",
       "9  Iranian negotiators reportedly have made a las...  REAL  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading the data\n",
    "df=pd.read_csv('/home/femme_js/Hoaxify/news.csv')\n",
    "\n",
    "df.head(10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Unnamed: 0  title   text  label\n",
      "0          False  False  False  False\n",
      "1          False  False  False  False\n",
      "2          False  False  False  False\n",
      "3          False  False  False  False\n",
      "4          False  False  False  False\n",
      "...          ...    ...    ...    ...\n",
      "6330       False  False  False  False\n",
      "6331       False  False  False  False\n",
      "6332       False  False  False  False\n",
      "6333       False  False  False  False\n",
      "6334       False  False  False  False\n",
      "\n",
      "[6335 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "# checking if column have nan values\n",
    "\n",
    "check_nan_in_df = df.isnull()\n",
    "print (check_nan_in_df)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# as data dont have any NaN value, we dont need to fill them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    FAKE\n",
       "1    FAKE\n",
       "2    REAL\n",
       "3    FAKE\n",
       "4    REAL\n",
       "Name: label, dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting the Labels\n",
    "\n",
    "labels=df.label\n",
    "labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8476</td>\n",
       "      <td>You Can Smell Hillary’s Fear</td>\n",
       "      <td>Daniel Greenfield, a Shillman Journalism Fello...</td>\n",
       "      <td>FAKE</td>\n",
       "      <td>You Can Smell Hillary’s Fear Daniel Greenfield...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10294</td>\n",
       "      <td>Watch The Exact Moment Paul Ryan Committed Pol...</td>\n",
       "      <td>Google Pinterest Digg Linkedin Reddit Stumbleu...</td>\n",
       "      <td>FAKE</td>\n",
       "      <td>Watch The Exact Moment Paul Ryan Committed Pol...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3608</td>\n",
       "      <td>Kerry to go to Paris in gesture of sympathy</td>\n",
       "      <td>U.S. Secretary of State John F. Kerry said Mon...</td>\n",
       "      <td>REAL</td>\n",
       "      <td>Kerry to go to Paris in gesture of sympathy U....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10142</td>\n",
       "      <td>Bernie supporters on Twitter erupt in anger ag...</td>\n",
       "      <td>— Kaydee King (@KaydeeKing) November 9, 2016 T...</td>\n",
       "      <td>FAKE</td>\n",
       "      <td>Bernie supporters on Twitter erupt in anger ag...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>875</td>\n",
       "      <td>The Battle of New York: Why This Primary Matters</td>\n",
       "      <td>It's primary day in New York and front-runners...</td>\n",
       "      <td>REAL</td>\n",
       "      <td>The Battle of New York: Why This Primary Matte...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                              title  \\\n",
       "0        8476                       You Can Smell Hillary’s Fear   \n",
       "1       10294  Watch The Exact Moment Paul Ryan Committed Pol...   \n",
       "2        3608        Kerry to go to Paris in gesture of sympathy   \n",
       "3       10142  Bernie supporters on Twitter erupt in anger ag...   \n",
       "4         875   The Battle of New York: Why This Primary Matters   \n",
       "\n",
       "                                                text label  \\\n",
       "0  Daniel Greenfield, a Shillman Journalism Fello...  FAKE   \n",
       "1  Google Pinterest Digg Linkedin Reddit Stumbleu...  FAKE   \n",
       "2  U.S. Secretary of State John F. Kerry said Mon...  REAL   \n",
       "3  — Kaydee King (@KaydeeKing) November 9, 2016 T...  FAKE   \n",
       "4  It's primary day in New York and front-runners...  REAL   \n",
       "\n",
       "                                               total  \n",
       "0  You Can Smell Hillary’s Fear Daniel Greenfield...  \n",
       "1  Watch The Exact Moment Paul Ryan Committed Pol...  \n",
       "2  Kerry to go to Paris in gesture of sympathy U....  \n",
       "3  Bernie supporters on Twitter erupt in anger ag...  \n",
       "4  The Battle of New York: Why This Primary Matte...  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combining important features into a single feature\n",
    "\n",
    "df['total'] = df['title'] + ' ' + df['text']\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PRE-PROCESSING THE DATA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8476</td>\n",
       "      <td>You Can Smell Hillary’s Fear</td>\n",
       "      <td>Daniel Greenfield, a Shillman Journalism Fello...</td>\n",
       "      <td>FAKE</td>\n",
       "      <td>smell hillary fear daniel greenfield shillman...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10294</td>\n",
       "      <td>Watch The Exact Moment Paul Ryan Committed Pol...</td>\n",
       "      <td>Google Pinterest Digg Linkedin Reddit Stumbleu...</td>\n",
       "      <td>FAKE</td>\n",
       "      <td>watch exact moment paul ryan committed politi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3608</td>\n",
       "      <td>Kerry to go to Paris in gesture of sympathy</td>\n",
       "      <td>U.S. Secretary of State John F. Kerry said Mon...</td>\n",
       "      <td>REAL</td>\n",
       "      <td>kerry go paris gesture sympathy u secretary s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10142</td>\n",
       "      <td>Bernie supporters on Twitter erupt in anger ag...</td>\n",
       "      <td>— Kaydee King (@KaydeeKing) November 9, 2016 T...</td>\n",
       "      <td>FAKE</td>\n",
       "      <td>bernie supporter twitter erupt anger dnc trie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>875</td>\n",
       "      <td>The Battle of New York: Why This Primary Matters</td>\n",
       "      <td>It's primary day in New York and front-runners...</td>\n",
       "      <td>REAL</td>\n",
       "      <td>battle new york primary matter primary day ne...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                              title  \\\n",
       "0        8476                       You Can Smell Hillary’s Fear   \n",
       "1       10294  Watch The Exact Moment Paul Ryan Committed Pol...   \n",
       "2        3608        Kerry to go to Paris in gesture of sympathy   \n",
       "3       10142  Bernie supporters on Twitter erupt in anger ag...   \n",
       "4         875   The Battle of New York: Why This Primary Matters   \n",
       "\n",
       "                                                text label  \\\n",
       "0  Daniel Greenfield, a Shillman Journalism Fello...  FAKE   \n",
       "1  Google Pinterest Digg Linkedin Reddit Stumbleu...  FAKE   \n",
       "2  U.S. Secretary of State John F. Kerry said Mon...  REAL   \n",
       "3  — Kaydee King (@KaydeeKing) November 9, 2016 T...  FAKE   \n",
       "4  It's primary day in New York and front-runners...  REAL   \n",
       "\n",
       "                                               total  \n",
       "0   smell hillary fear daniel greenfield shillman...  \n",
       "1   watch exact moment paul ryan committed politi...  \n",
       "2   kerry go paris gesture sympathy u secretary s...  \n",
       "3   bernie supporter twitter erupt anger dnc trie...  \n",
       "4   battle new york primary matter primary day ne...  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "stop_words = stopwords.words('english')\n",
    "\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    filter_sentence = ''\n",
    "    sentence = row['total']\n",
    "    # Cleaning the sentence with regex\n",
    "    sentence = re.sub(r'[^\\w\\s]', '', sentence)\n",
    "    # Tokenization\n",
    "    words = nltk.word_tokenize(sentence)\n",
    "    # Stopwords removal\n",
    "    words = [w for w in words if not w in stop_words]\n",
    "    # Lemmatization\n",
    "    for words in words:\n",
    "        filter_sentence = filter_sentence  + ' ' + str(lemmatizer.lemmatize(words)).lower()\n",
    "    \n",
    "    df.loc[index, 'total'] = filter_sentence\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     smell hillary fear daniel greenfield shillman...\n",
       "1     watch exact moment paul ryan committed politi...\n",
       "2     kerry go paris gesture sympathy u secretary s...\n",
       "3     bernie supporter twitter erupt anger dnc trie...\n",
       "4     battle new york primary matter primary day ne...\n",
       "Name: total, dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df['label']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f65b3e72978>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['label'].value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['FAKE', 'REAL'], dtype=object)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.label = df.label.astype(str)\n",
    "df.label.unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "Name: label, dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.label = df.label.astype(str)\n",
    "df.label = df.label.str.strip()\n",
    "\n",
    "\n",
    "dict = { 'REAL' : '1' , 'FAKE' : '0'}\n",
    "\n",
    "df['label'] = df['label'].map(dict)\n",
    "\n",
    "df['label'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_df = df['total']\n",
    "y_df = df['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     smell hillary fear daniel greenfield shillman...\n",
       "1     watch exact moment paul ryan committed politi...\n",
       "2     kerry go paris gesture sympathy u secretary s...\n",
       "3     bernie supporter twitter erupt anger dnc trie...\n",
       "4     battle new york primary matter primary day ne...\n",
       "Name: total, dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "Name: label, dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#VECOTRIZATION\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 79944)\t0.029961665151307385\n",
      "  (0, 79749)\t0.018595474099453224\n",
      "  (0, 79395)\t0.036615467772519145\n",
      "  (0, 79376)\t0.02355747992988494\n",
      "  (0, 79332)\t0.03594384891128402\n",
      "  (0, 79289)\t0.02697568668358886\n",
      "  (0, 79282)\t0.019878140017837338\n",
      "  (0, 79278)\t0.00860342274360593\n",
      "  (0, 79115)\t0.01570600645390605\n",
      "  (0, 79014)\t0.01438969583128738\n",
      "  (0, 78914)\t0.027881406253180738\n",
      "  (0, 78913)\t0.016495809217904393\n",
      "  (0, 78845)\t0.038416858595659395\n",
      "  (0, 78761)\t0.024355075927542776\n",
      "  (0, 78610)\t0.034694878207868526\n",
      "  (0, 78517)\t0.03515710620069247\n",
      "  (0, 78488)\t0.0180895260083385\n",
      "  (0, 78342)\t0.013680144757713397\n",
      "  (0, 78300)\t0.018725162203103333\n",
      "  (0, 78293)\t0.0611263997401894\n",
      "  (0, 78034)\t0.01136718115233016\n",
      "  (0, 77986)\t0.030504187362211135\n",
      "  (0, 77868)\t0.04229651595781814\n",
      "  (0, 77761)\t0.032266387789282994\n",
      "  (0, 77687)\t0.023218154073912735\n",
      "  :\t:\n",
      "  (6334, 9116)\t0.034961446302379004\n",
      "  (6334, 9103)\t0.09675332285419716\n",
      "  (6334, 9098)\t0.0665954515387032\n",
      "  (6334, 9096)\t0.09367722226789081\n",
      "  (6334, 8524)\t0.017506370821624376\n",
      "  (6334, 8437)\t0.032934465710185316\n",
      "  (6334, 8392)\t0.03285946712960298\n",
      "  (6334, 8364)\t0.026421841129276452\n",
      "  (6334, 8160)\t0.05153619030502903\n",
      "  (6334, 8097)\t0.03878242545700508\n",
      "  (6334, 8071)\t0.025725569688909215\n",
      "  (6334, 8069)\t0.026437396261267423\n",
      "  (6334, 8064)\t0.05380688226235036\n",
      "  (6334, 7944)\t0.03420695909627367\n",
      "  (6334, 7280)\t0.030962779833488763\n",
      "  (6334, 7186)\t0.03791674404047929\n",
      "  (6334, 6735)\t0.02324823316786639\n",
      "  (6334, 6441)\t0.032711727539673545\n",
      "  (6334, 5861)\t0.02871980380697055\n",
      "  (6334, 5497)\t0.03232012030466833\n",
      "  (6334, 5037)\t0.016582915320627552\n",
      "  (6334, 3644)\t0.05681576883972211\n",
      "  (6334, 2203)\t0.026594785511428735\n",
      "  (6334, 2181)\t0.044954422402769124\n",
      "  (6334, 1838)\t0.01499847407259141\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "\n",
    "\n",
    "count_vectorizer = CountVectorizer()\n",
    "count_vectorizer.fit_transform(x_df)\n",
    "freq_term_matrix = count_vectorizer.transform(x_df)\n",
    "\n",
    "tfidf = TfidfTransformer(norm = \"l2\")\n",
    "tfidf.fit(freq_term_matrix)\n",
    "tf_idf_matrix = tfidf.fit_transform(freq_term_matrix)\n",
    "\n",
    "print(tf_idf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting data into train and test data\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(tf_idf_matrix,\n",
    "                                   y_df, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Implementing DIfferent Models and checking accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1957    1\n",
       "5016    1\n",
       "3166    1\n",
       "811     0\n",
       "3389    1\n",
       "Name: label, dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91.16161616161617\n"
     ]
    }
   ],
   "source": [
    "#LOGISTIC REGRESSION\n",
    "\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(x_train, y_train)\n",
    "Accuracy = logreg.score(x_test, y_test)\n",
    "\n",
    "print(Accuracy*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82.32323232323232\n"
     ]
    }
   ],
   "source": [
    "#NAIVE BAYES\n",
    "\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "\n",
    "NB = MultinomialNB()\n",
    "NB.fit(x_train, y_train)\n",
    "Accuracy = NB.score(x_test, y_test)\n",
    "\n",
    "print(Accuracy*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80.49242424242425\n"
     ]
    }
   ],
   "source": [
    "# DECISION TREE\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "\n",
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(x_train, y_train)\n",
    "Accuracy = clf.score(x_test, y_test)\n",
    "\n",
    "print(Accuracy*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 93.12%\n"
     ]
    }
   ],
   "source": [
    "# PASSIVE-AGGRESSIVE CLASSIFIER\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from sklearn.linear_model import PassiveAggressiveClassifier\n",
    "\n",
    "#DataFlair - Initialize a PassiveAggressiveClassifier\n",
    "pac=PassiveAggressiveClassifier(max_iter=50)\n",
    "pac.fit(x_train,y_train)\n",
    "#DataFlair - Predict on the test set and calculate accuracy\n",
    "y_pred=pac.predict(x_test)\n",
    "score=accuracy_score(y_test,y_pred)\n",
    "\n",
    "print(f'Accuracy: {round(score*100,2)}%')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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